Method and device for multimodal neurological evaluation

ABSTRACT

A method of building classifiers for multimodal neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of physiological and neurocognitive assessments, and selecting a subset of features from the extracted pool of features to construct multimodal classifiers. A device for performing point-of-care multimodal neurological assessment is also described.

This application is a continuation of application Ser. No. 13/352,618,filed Jan. 18, 2012, which is incorporated herein by reference in itsentirety.

The present disclosure relates to the field of neurological assessment,and specifically, to the development of a method and device forcombining the results from multiple assessment technologies to provide amulti-dimensional evaluation of a subject's neurological condition.

Currently, objective assessment of brain function is limited toevaluation of a subject's brain electrical activity data collectedthrough EEG (electroencephalography) recording. At a basic level, thebrain electrical signals serve as a signature for both normal andabnormal brain function, and an abnormal brain wave pattern can be astrong indication of certain brain pathologies.

Objective assessment of brain electrical signals may be performed usinga classifier that provides a mathematical function for mapping (orclassifying) a vector of quantitative features extracted from therecorded EEG data into one or more predefined categories. Classifiersare built by forming a training dataset, where each subject is assigneda “label,” namely a neurological class based on information provided bydoctors and obtained with the help of state-of-the-art diagnosticsystems, such as CT scan, MRI, etc. For each subject in the dataset, alarge set of quantitative signal attributes or features (computed fromthe EEG) is also available. The process of building a classifier from atraining dataset involves the selection of a subset of features (fromthe set of all quantitative features), along with the construction of amathematical function which uses these features as input and whichproduces as its output an assignment of the subject's data to a specificclass. After a classifier is built, it may be used to classify unlabeleddata records as belonging to one or the other potential neurologicalclasses. Classification accuracy is then reported using a testingdataset which may or may not overlap with the training set, but forwhich a priori classification data is also available. The accuracy ofthe classifier is dependent upon the selection of features that comprisepart of the specification of the classifier. Selection of features thatcontribute most to the classification task ensures the bestclassification performance.

Although brain electrical activity data provides a valuable means foranalyzing brain function, the presence or severity of certainheterogeneous types of brain injury or dysfunction, for example,traumatic brain injury (TBI), can be assessed more objectively bycombining the results from a plurality of diagnostic tests. Accordingly,the present disclosure provides a method of expanding the classifierbuilding process to integrate features or outputs from multipleassessment technologies into the feature selection process. Theinclusion of features from multiple assessment technologies holds thepromise of improving classification performance beyond that achievedwith features derived only from brain electrical signals.

The present disclosure describes a method of building a classificationsystem for real-time evaluation of a subject's neurological state,wherein the classification system combines the results/outputs frommultiple assessment technologies to perform multimodal assessment of thesubject's condition. A first aspect of the present disclosure comprisesa method of building a classifier for multimodal assessment of asubject's neurological condition. The method comprises the step ofproviding a signal processing device operatively connected to a memorydevice which stores results of different assessments performed on aplurality of individuals in the presence or absence of brainabnormalities. The signal processing device comprises a processorconfigured to obtain results of two or more different assessments fromthe memory device, extract quantitative features from the results of thetwo or more assessment, store the extracted features in a pool ofselectable features, select a subset of features from the pool ofselectable features to construct the classifier, and determineclassification accuracy of the classifier by using it to classify datarecords having a priori classification information.

A second aspect of the present disclosure is another method of buildinga classifier for classification of individual data into one of two ormore categories of neurological condition. The method comprises thesteps of providing a processor configured to build a classifier, andproviding a memory device operatively coupled to the processor, whereinthe memory device stores a population reference database comprising apool of quantitative features extracted from the results of two or morephysiological and neurocognitive assessments performed on a plurality ofindividuals in the presence or absence of brain abnormalities. Theprocessor is configured to select a plurality of features from brainelectrical activity data and one or more other assessments performed onthe plurality of individuals in the population reference database,constructing a classifier using the selected quantitative features, andevaluating performance of the classifier using pre-labeled data recordsthat are assigned a priori to one of the two categories.

A yet another aspect of the disclosure is a device for performingmultimodal assessment of a subject's neurological condition. The deviceincludes a headset comprising one or more neurological electrodesconfigured to record the subject's brain electrical activity, an inputdevice configured to acquire results of one or more physiological andneurocognitive assessments performed on the subject, and a base unitoperatively coupled to the headset and the input device. The base unitcomprises a processor configured to perform the steps of extractingquantitative features from the brain electrical activity data and theother physiological and neurocognitive assessments performed on thesubject, and further applying a multimodal classifier to performclassification of the subject's neurological condition into one of twoor more categories.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed. The terms “EEGsignal” and “brain electrical signal” are used interchangeably in thisapplication to mean signals acquired from the brain using neurologicalelectrodes.

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles of thevarious aspects of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a prior art approach to combining results of multipleassessments;

FIG. 2 is a flowchart of a multimodal classifier building process, inaccordance with an exemplary embodiment of the present disclosure; and

FIG. 3 illustrates a multimodal neuroassessment apparatus, in accordancewith exemplary embodiments of the present disclosure.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Reference will now be made in detail to certain embodiments consistentwith the present disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

Multimodal Classifier Building Method

The present disclosure describes a method for building a classifier formapping multi-modal assessment data into one or more predefinedneurological classes or categories. In an exemplary embodiment, themultiple assessment technologies include various neurophysiologicalassessments tools, for example, EEG recording, infrared testing to lookfor blood in the head, clinical testing of biomarkers that indicatebrain injury, reaction time testing, eye movement tracking, etc. Inanother exemplary embodiment, the multiple assessment technologiesadditionally include neurocognitive assessment (such as, StandardizedAssessment of Concussion (SAC), Automated Neurophysiological AssessmentMetrics (ANAM), ImPACT, etc.). In yet another exemplary embodiment, themultiple assessment technologies further include other physiologicaltesting, such as, electrocardiography (ECG or EKG), heart ratevariability testing, galvanic skin response testing, etc. The resultsprovided by the multiple assessment technologies are integrated toprovide the best classification or assessment performance.

In a conventional approach to combining results of multiple assessments,the outputs of two or more technologies are combined using an algorithm,such as, tree logic, voting methods, or weighted combinations, etc., toprovide a combined result, as illustrated in FIG. 1. This is analogousto a physician using the results of multiple tests to diagnose apatient's condition. The result or output from each assessment isprovided as an input to the combination algorithm, which is applied toeach subject to make an overall classification or assessment of thesubject's neurological state. In contrast, the present disclosuredescribes a method that enables inclusion of results/outputs frommultiple technologies as selectable features in the algorithmdevelopment process. The integration of multimodal assessment data inthe algorithm development process offers a distinct advantage inmulti-class classification applications where the results of certainassessment technologies are not relevant to all of the classes. In suchcases, the inclusion of extraneous assessments could potentially distortthe overall classification result. The method described in the presentdisclosure overcomes this disadvantage by enabling the training of theclassification algorithm to identify results/features (from allavailable assessment results) that contribute the most to eachclassification task.

An exemplary classifier building methodology is illustrated in FIG. 2.The classifier building algorithm, as illustrated in FIG. 2, is executedby a signal processing device comprising a processor. An initial step inthe classifier building process is the collection of raw brainelectrical signals (step 201). In an exemplary embodiment, a subject'selectrical brain activity is recorded using a varying number ofnon-invasive neurological electrodes located at standardized positionson the scalp and forehead and ear-lobes. In one exemplary embodiment, asubject's brain electrical activity is recorded using an electrode arraycomprising at least one neurological electrode to be attached to apatient's head to acquire the brain electrical signals. The electrodesare configured for sensing both spontaneous brain activity as well asevoked potentials generated in response to applied stimuli (e.g.auditory, visual, tactile stimuli, etc.). In exemplary embodiments, thesignal processor running the classifier building algorithm is configuredto implement an artifact detection algorithm to identify data that iscontaminated by non-brain-generated artifacts, such as eye movements,electromyographic activity (EMG) produced by muscle tension, spike(impulse), external noise, etc., as well as unusual electrical activityof the brain not part of the estimation of stationary background state.An exemplary artifact detection method is described in U.S. applicationSer. No. 13/284,184, which is incorporated herein by reference in itsentirety.

The artifact-free data epochs are then processed to extract quantitativesignal features (step 3), in an exemplary embodiment, the processor isconfigured to perform a linear feature extraction algorithm based onFast Fourier Transform (FFT) and power spectral analysis, according to amethod disclosed in commonly-assigned U.S. Pat. Nos. 7,720,530 and7,904,144, which are incorporated herein by reference in their entirety.In another embodiment, the processor is configured to perform featureextraction based on wavelet transforms, such as Discrete WaveletTransform (DWT) or Complex Wavelet Transforms (CWT). In yet anotherembodiment, the processor is configured to perform feature extractionusing non-linear signal transform methods, such as wavelet packettransform, according to a method disclosed in commonly-assigned U.S.patent application Ser. No. 12/361,174, which is incorporated herein byreference in its entirety. The features extracted by this method arereferred to as Local Discriminant Basis (LDB) features. In anotherembodiment, diffusion geometric analysis is used to extract non-linearfeatures according to a method disclosed in commonly-assigned U.S.patent application Ser. No. 12/105,439, which is incorporated herein byreference in its entirety. In yet other embodiments, entropy, fractaldimension, and mutual information-based features are also calculated.

In exemplary embodiments, the computed measures per epoch are combinedinto a single measure of EEG signal per channel and transformed forGaussianity. Once a Gaussian distribution has been demonstrated and ageregression applied, statistical Z transformation is performed to produceZ-scores. The Z-transform is used to describe the deviations from ageexpected normal values:

$Z = \frac{{{Subject}\mspace{14mu}{Value}} - {{Norm}\mspace{14mu}{for}\mspace{14mu}{Age}}}{{Standard}\mspace{14mu}{Deviation}\mspace{14mu}{for}\mspace{14mu}{Age}}$

The Z-scores are calculated for each feature and for each electrode,pair of electrodes, or pair of a pair of electrodes, using a database ofresponse signals from a large population of subjects believed to benormal, or to have other pre-diagnosed conditions. In particular, eachextracted feature is converted to a Z-transformed score, whichcharacterizes the probability that the extracted feature observed in thesubject will conform to a normal value. The age-regressed andZ-transformed signal features are stored in a population referencedatabase. The database is stored in a memory device that isoperationally coupled to the signal processor executing the classifierbuilding algorithm.

Referring again to FIG. 2, the next step in the algorithm developmentprocess is the collection of results from other assessment modalities(step 5) and extraction of quantitative features from the otherassessment results (step 7). For example, in one embodiment, themultimodal assessments include reaction time testing. One or morequantitative features are calculated from the results of the reactiontime testing. Reaction time test looks at the time it takes a subject torespond to an applied stimuli (visual, auditory, etc.) and compares itto a normative value. Quantitative features are calculated from theresults of multiple trials (e.g., the mean and standard deviation ofmultiple test results). In exemplary embodiments, the features ofinterest are z-scored and stored in the population reference databasefor use in the algorithm development process. Similarly, quantitativefeatures are calculated from the output of other assessment technologies(e.g., EKG, galvanic skin reaction testing, etc.). The features aretransformed into z-scores and stored in the population referencedatabase.

In an exemplary embodiment, neurocognitive assessment is performedalongside the physiological evaluations. In some embodiments, theneurocognitive assessment is performed using standardized questionnairesfor testing the presence or severity of neurocognitive impairmentfollowing an injury, such as, the Standardized Assessment of Concussion(SAC), Automated Neurophysiological Assessment Metrics (ANAM), ImPACT,etc. In other embodiments, the neurocognitive assessment is performedusing a dynamic questionnaire created from expert neuropsychologicalassessment practices. The questionnaire is designed to dynamically adaptto responses given by a subject, i.e., each subject may not be askedexactly the same set of questions. Data from the neurocognitiveassessment is processed to extract quantitative features, for example,reaction time metrics, severity ranking of symptoms, normalizedcombinations of symptoms or clinical manifestations, etc., which areentered into the pool of selectable features in the reference database.In case of the dynamic questionnaire, the features that are entered intothe database are based on the overall quantitative output from thequestionnaire and not based on any specific assessment metrics.

Once features are extracted from the various assessment technologies andstored in the reference database, the next step in the algorithmdevelopment process in the selection of features that provide the bestclassification performance (step 8). The weights and constants thatdefine a classification function (such as, Linear Discriminant Function,Quadratic Discriminant Function, etc.) are derived from a set ofquantitative features in the population reference database. Thus, thedesign or construction of a classification function targeting anyclassification task (e.g. “Normal” vs. “Abnormal” brain function)requires selection of a set of features from a large available pool offeatures in the population reference database. The selection of the“best” features results in the “best” classification performance,characterized by, for example, the highest sensitivity/specificity andlowest classification error rates. In order to make the featureselection process more efficient and to ensure higher classificationperformance, the available pool of features from the multiple assessmentmodalities must be transformed or reduced to a computationallymanageable and neurophysiologically relevant pool of features from whicha subset of features for a particular classification task may beselected during classifier construction.

Accordingly, in some exemplary embodiments, the pool of availablefeatures in the population reference database is reduced to a smallerset of features that contribute directly to a specific classificationtask. In an exemplary embodiment, a reduced pool of features is createdusing an “informed data reduction” technique, which relies on thespecific downstream application of the classifier, neurophysiologyprinciples and heuristic rules. In exemplary embodiments, the “informeddata reduction” method includes several different criteria to facilitatethe inclusion of features that most effectively provide separation amongthe classes. The “informed data reduction” method is described in U.S.application Ser. No. 13/284,184, which is incorporated herein byreference.

Once all the data reduction criteria are applied, the remaining reducedpool of features is utilized to design a classifier (step 9). In oneexemplary embodiment, the classifier is a binary classifier used toclassify individual data records as belonging to one of two classes. Inanother exemplary embodiment, the classifier is a multiclass classifierused to classify data records into more than two classes. In yet anotherexemplary embodiment, a series of binary classifiers that use eitherlinear or non-linear discriminant functions are used to classifyindividuals into multiple categories. In some embodiments, x-1discriminant functions are used to separate individual subjects into xclassification categories. In an exemplary embodiment, three binaryclassifiers are designed and implemented for classifying patients Intoone of four categories related to the extent of brain dysfunctionresulting from a traumatic brain injury (TBI), as described in U.S.application Ser. No. 12/857,504, which is incorporated herein byreference.

The construction of a classifier is now described with reference to abinary classifier. In exemplary embodiments, a binary classifier isdesigned by selecting a specific set of features for each discriminantfunction based on performance. The search for the “best” features for abinary classification task is performed using a fully-automated system(hereinafter “classifier builder”), implemented as a computer program,the output of which is a Discriminant Function classifier. In exemplaryembodiments, identification of the “best” features for a particularclassification task is performed by computing multiple classifiers usingdifferent combination of features, and evaluating each possibleclassifier using an “objective function” that is directly related toclassification performance. In an exemplary embodiment, the objectivefunction (figure of merit) used by a feature selection algorithm is thearea under the Receiver Operating Characteristics (ROC) curve of aDiscriminant Function, which is usually referred to as “Area Under theCurve” (AUC). For a given discriminant-based binary classifier, the ROCcurve indicates the sensitivity and specificity that can be expectedfrom the classifier at different values of the classification thresholdT. Once a critical value (or threshold) T is selected, the output of thetest becomes binary, and sensitivity and specificity for that particularthreshold can be calculated. The ROC is the curve through the set ofpoints: {(1-specificity(T), sensitivity(T))}, which is obtained byvarying the value of the threshold T in fixed increments between 0 and100. After the ROC curve is obtained, the area under the ROC curve (AUC)is calculated. AUC is a single number between 0 and 1, which reflects,jointly, the sensitivity and specificity of a binary classifier. Thus,AUC provides a quantitative global measure of achievable classifierperformance.

In one exemplary embodiment, the search for the “best” features for aclassification task is performed using a feature selection algorithmthat is referred to herein as “Simple Feature Picker” (SFP) algorithm.The SFP algorithm selects a first feature by evaluating all features inthe database or the reduced pool of features, and selects the featurethat provides the best classifier performance. Subsequent features areselected to give the best incremental improvement in classifierperformance. In another exemplary embodiment, the SFP algorithm addsmultiple features to the classifier at each iteration, calculates AUC ofthe resulting classifier at each iteration step, and selects thefeatures that provide that greatest improvement in AUC.

In another exemplary embodiment, feature selection is performed usingone or more evolutionary algorithms, for example, a Genetic Algorithm(GA), as described in commonly-owned U.S. application Ser. No.12/541,272 which is incorporated herein by reference in its entirety. Inyet another exemplary embodiment, the search for candidate features isperformed using an optimization method, for example, Random MutationHill-Climbing (RMHC) method, or Modified Random Mutation Hill Climbing(mRMHC), which can be used in a stand-alone fashion or can be combinedwith the GA algorithm or SFP algorithm (for example, as a final “localsearch” to replace one feature by another to improve the final featuresubset), as further described in the U.S. application Ser. No.12/541,272 incorporated herein.

After a classifier is built, classification accuracy is evaluated usinga testing dataset comprising pre-labeled data records which are assigneda priori to one of the classification categories. In some embodiments,the testing dataset is separate from the training set. In some otherexemplary embodiments, all available data is used for both training andtesting of the classifier. In such embodiments, performance of theclassifier is evaluated using 10-fold and/or leave-one-out (LOO)cross-validation methods, as described in U.S. application Ser. Nos.12/857,504, and 13/284,184, which are incorporated herein by reference.After a classifier is built and tested for accuracy, it may be used toclassify unlabeled data records (i.e., unknown subjects) as belonging toa particular class.

Portable Device for Field Applications

Another aspect of the present disclosure is an apparatus for performingmultimodal neurological triage on a subject, FIG. 3 illustrates amultimodal neuro-assessment apparatus 10, in accordance with exemplaryembodiments of the present disclosure. In an exemplary embodiment, theneuro-assessment apparatus 10 is implemented as a portable device forpoint-of-care applications. The apparatus consists of a headset 40 whichmay be coupled to a base unit 42, which can be handheld. Headset 40 mayinclude a plurality of electrodes 35 to be attached to a patient's headto acquire brain electrical signals. The electrodes are configured forsensing both spontaneous brain activity as well as evoked potentialsgenerated in response to applied stimuli, such as audio, tactile, orelectrical stimuli. In an exemplary embodiment, recording is done usingfive (active) channels and three reference channels. The electrode arrayconsists of anterior (frontal) electrodes: Fp1, Fp2, F7, F8, AFz (alsoreferred to as Fz′) and Fpz (reference electrode) to be attached to asubject's forehead, and electrodes A1 and A2 to be placed on the frontor back side of the ear lobes, or on the mastoids, in accordance withthe International 10/20 electrode placement system (with the exceptionof AFz). Other electrode configurations may be utilized as and whenrequired, as would be understood by those of ordinary skill in the art.The use of a limited number of electrodes enable rapid and repeatableplacement of the electrodes on a subject, which in turn facilitatesefficient, and more accurate, patient monitoring. Further, in oneembodiment, the electrodes may be positioned on a low-cost, disposableplatform, which can serve as a “one-size-fits-all” sensor. For example,electrodes 35 may be positioned on a head gear that is configured foreasy and/or rapid placement on a patient, as further set forth incommonly assigned U.S. patent application Ser. No. 12/059,014, which isincorporated herein by reference in its entirety. Other electrodeconfigurations may be utilized as and when required, as would beunderstood by those of ordinary skill in the art.

In an exemplary embodiment, the neuro-assessment apparatus 10 utilizesthe advantages of auditory evoked potential (AEP) signals to mapspecific auditory, neurological and psychiatric dysfunctions. In such anembodiment, the headset 40 includes reusable earphone 31 to provideauditory stimuli clicks in either ear. In some embodiments, the auditoryevoked potential signal used is auditory brainstem response (ABR). Insuch embodiments, the auditory stimuli may be delivered at 100 dBPeak-to-Peak Equivalent Sound Pressure Level and at a frequency (rate)of 27 Hz (27 clicks per second) to evoke electrical signals from thebrainstem in response to the applied auditory stimuli. Other auditorystimuli may also be used, to evoke mid-latency (20-80 milliseconds) orlate auditory responses (>80 milliseconds), including the P300. Inanother embodiment, headset 40 may include an additional ear phone todeliver white noise in the other ear.

In another exemplary embodiment, the neuro-assessment device 10 utilizesvisual evoked potentials (VEP) to evaluate the extent of brain injury ordysfunction. For example, in some embodiments, VEP is used to evaluatepost-trauma vision syndrome (PTVS) in patients with traumatic braininjuries (TBI). In one exemplary embodiment, monocular and binocularVEPs are recorded under various stimulus conditions provided by the baseunit 42 through display 44. In another exemplary embodiment, headset 40includes a pair of goggles to provide visual stimuli to patients. In onesuch embodiment, the goggles are mounted with light emitting diodes(LEDs) to provide flash stimuli to elicit VEPs. In another embodiment,the goggles are mounted with a video monitor to provide pattern stimulito patients.

In addition to acquiring brain electrical signals, neuro-assessmentapparatus 10 is designed to collect the output from other assessmenttechnologies. In one embodiment, the results from other assessmentmodalities are manually entered by the user. In another embodiment, theresults are acquired electronically via wireless or other communicationmethods. In yet another embodiment, apparatus 10 comprises an accessorydevice for administering a test and acquiring results. For example, insome embodiments, reaction time testing is performed using an inputdevice, such as, a graphical user interface, which is independent ofuser interface 46. The independent input device is used to minimizelatency errors, and thereby improve reaction time measurements.

Referring back to FIG. 3, display 44 in the base unit 42 comprises a LCDscreen, and can further have a user interface 46, which can be a touchscreen user interface or a traditional keyboard-type interface.Communication link 41 can act as a multi-channel input/output interfacefor the headset 40 and the handheld device 42, to facilitatebidirectional communication of signals to and from the processor 50,such that, for example, a command from the user entered through the userinterface 46 can start the signal acquisition process of the headset 40.Communication link 41 may include a permanently attached or detachablecable or wire, or may include a wireless transceiver, capable ofwirelessly transmitting signals and receiving signals from the headset,or from an external device storing captured signals. In exemplaryembodiments, communication link 41 includes two reusable patientinterface cables which are designed to plug into the base unit 42 andprovide direct communication between the headset 40 and base unit 42.The first cable is an electrical signal cable 41 a, which is equippedwith standard snap connectors to attach to the disposable electrodesplaced on the patient's scalp. The second cable is the AEP stimuluscable 41 b which provides connection to the earphone 31 for auditorystimulus delivery. In some embodiments, the headset 40 includes analogamplification channels connected to the electrodes, and ananalog-to-digital converter (ADC) to digitize the acquired brainelectrical signals prior to receipt by the base unit 42.

In an exemplary embodiment, the brain electrical activity data and theresults of the other physiological and neurocognitive assessmenttechnologies are processed by signal processor 50 in the hand-held baseunit 42. Processor 50 is configured to perform real-time evaluation of asubject's neurological state using instructions stored in memory device52. In exemplary embodiments, processor 50 is configured to apply one ormore multimodal classifiers to combine the results/outputs from aplurality of assessment technologies and provide a multi-dimensionalevaluation of the subject's condition. In one such embodiment, processor50 is configured to extract quantitative features from the results ofthe physiological and neurocognitive assessments, and apply one or morediscriminant functions to classify an unknown subject as belonging toone of two or more neurological categories.

In illustrative embodiments, memory device 52 contains interactiveinstructions for using and operating the device to be displayed onscreen 44. The instructions may comprise an interactive feature-richpresentation including a multimedia recording providing audio/videoinstructions for operating the device, or alternatively simple text,displayed on the screen, illustrating step-by-step instructions foroperating and using the device. The inclusion of interactiveinstructions with the device eliminates the need for extensive usertraining, allowing for deployment and uses by persons other than medicalprofessional. In some embodiments, the memory 52 may also contain thepopulation reference database. In other embodiments, the referencedatabase may be accessed from a remote storage device via a wireless ora wired connection. Further, in some exemplary embodiments, memory 52includes a dynamic software designed to lead a medical personnel withminimal training through a step-by-step assessment of a subject. Thesoftware is designed to present assessment questions to the user basedon responses provided by the subject to prior questions. The questionsare designed to guide the user through the various available assessmenttools.

The classification result obtained from processor 50 is displayed on thedisplay screen 44, or saved in external memory or data storage device47, or displayed on a PC 48 connected to the base unit 42. In oneembodiment, base unit 42 contains a wireless transceiver to transmit theresults wirelessly to a remote network or PC 48, or the external memory47 to store the results. In some embodiments, the neuro-assessmentapparatus 10 can also transmit data to another mobile or stationarydevice to facilitate more complex data processing or analysis. Forexample, the neuro-assessment device, operating in conjunction with PC48, can send data to be further processed by the computer. In anotherembodiment, the results can be transmitted wirelessly or via a cable toa printer 49 that prints the results. Base unit 42 can also contain aninternal rechargeable battery 43 that can be charged during or inbetween uses by battery charger 39 connected to an AC outlet 37. Thebattery can also be charged wirelessly through electromagnetic couplingby methods known in the prior art. In some embodiments, the base unit 42further includes a stimulus generator 54 for applying stimuli to thesubject for AEP measurement, or for reaction time measurement. In someembodiments, the stimulus generator is included in the headset 40.

In another exemplary embodiment, user interface 46 receives and displaysusage setting information, such as the name, age and/or other statisticspertaining to the patient. In some embodiments, the user interface 46comprises a touchscreen for entering the user input. A virtual keypadmay be provided on the touchscreen interface for input of patientrecord.

The neuro-assessment apparatus 10 is designed for near-patient testingin emergency rooms, ambulatory setting, and other field applications.The neuro-assessment device is intended to be used in conjunction withCT scan, MRI or other imaging studies to provide complementary orcorroborative information about a patient's neurological condition. Thekey objective of point-of-care neuro-assessment is to provide fastresults indicating the severity of a patient's neurological condition,so that appropriate treatment can be quickly provided, leading to animproved overall clinical outcome. For example, neuro-assessment device10 may be used by an EMT, ER nurse, or any other medical professionalduring an initial patient processing in the ER or ambulatory setting,which will assist in identifying the patients with emergencyneurological conditions. It will also help ER physicians incorroborating an immediate course of action, prioritizing patients forimaging, or determining if immediate referral to a neurologist orneurosurgeon is required. This in turn will also enable ER personnel tooptimize the utilization of resources (e.g., physicians' time, use ofimaging tests, neuro consults, etc.) in order to provide safe andimmediate care to all patients.

In addition, neuro-assessment device 10 is designed to befield-portable, that is, it can be used in locations far removed from afull-service clinic—for example, in remote battlefield situationsdistant from military healthcare systems, during sporting events foridentifying if an injured athlete should be transported for emergencytreatment, at a scene of mass casualty in order to identify patients whoneed critical attention and immediate transport to the hospital, or atany other remote location where there is limited access to well-trainedmedical technicians.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

The invention claimed is:
 1. A device for multimodal assessment of asubject's neurological condition, the device comprising: a headsetcomprising one or more neurological electrodes configured to record thesubject's brain electrical signals; a base unit operatively coupled tothe headset and configured to acquire results of one or morephysiological assessments and/or neurocognitive assessments performed onthe subject, the base unit further comprising a processor configured toperform the steps of: extracting quantitative features from the brainelectrical signals; extracting quantitative features from the results ofthe one or more physiological assessments and/or neurocognitiveassessments performed on the subject; and applying a multimodalclassifier to provide a multi-dimensional evaluation of the subject'sneurological condition, wherein the multimodal classifier is configuredto combine the extracted quantitative features from the brain electricalsignals and extracted quantitative features from the results of the oneor more physiological assessments and/or neurocognitive assessments, andfurther wherein the multimodal classifier is pre-developed andpre-trained using selectable features from a training dataset comprisingbrain electrical activity data and one or more physiological assessmentsand/or neurocognitive assessments data obtained from a plurality ofindividuals in the presence or absence of brain abnormalities.
 2. Thedevice of claim 1, wherein the processor is configured to performautomatic identification and removal of artifacts from the brainelectrical signals.
 3. The device of claim 1, wherein the base unit isoperatively coupled to an input device, and wherein the input device isconfigured to acquire the results of the one or more physiologicalassessments and/or neurocognitive assessments.
 4. The device of claim 1,wherein the base unit is configured to wirelessly receive the results ofthe one or more physiological assessments and/or neurocognitiveassessments.
 5. The device of claim 1, further comprising a memorydevice for storing interactive instructions for using and operating thedevice.
 6. The device of claim 5, wherein the memory device stores adynamic questionnaire that provides step-by-step instructions forperforming the multimodal assessment.
 7. The device of claim 1, furthercomprising a touch-screen interface to enter user input.
 8. The deviceof claim 1, wherein the base unit is a handheld device.
 9. The device ofclaims 1, wherein the one or more physiological and/or neurocognitiveassessments comprise recording of electrocardiographic signals.
 10. Thedevice of claim 1, wherein the multimodal classifier comprises one ormore discriminant functions.
 11. The device of claim 1, wherein themultimodal classifier is a binary classifier.
 12. The device of claim 1,wherein the multimodal classifier is a multi-class classifier.
 13. Thedevice of claim 1, further comprising a monitor for providing visualstimuli to the subject to elicit visual evoked potentials.
 14. Thedevice of claim 1, further comprising a pair of goggles for providingvisual stimuli to the subject to elicit visual evoked potentials. 15.The device of claim 1, further comprising one or more earphones toprovide auditory stimuli to the subject to elicit auditory evokedpotentials.
 16. The device of claim 1, wherein the base unit isconfigured to transmit data to another mobile or stationary device tofacilitate more complex data processing or analysis.
 17. The device ofclaim 1, wherein the base unit comprises a display screen to display aresult of the multimodal assessment of the subject's neurologicalcondition.
 18. The device of claim 1, wherein the base unit comprises awireless transceiver to transmit a result of the multimodal assessmentwirelessly to a remote network, computer, or to an external memorydevice to store the result.